In the immediate hours following disaster, information is sparse; however, as time progresses, analysts receive data about the evolving state and impact on various geographical areas. From the chaos, decision makers must distill the critical information needed to target aid most efficiently.
Geographical Information Systems (GIS) are collaborative systems that collect and process data for individuals conducting ground observations and surveys, including data about facilities. GIS data relevant to millions of system variables may be sent from handheld GPS units, personal computing devices, digital cameras, aerial photo satellites, voice recorders and a range of devices which have GPS capability.
Recent disasters have inspired the development of several collaborative GIS tools to collect and map these new sources of information, including OpenStreetMap, Sahana, and Crisis Mappers. It is necessary to structure information from many heterogeneous sources into key variables and metrics most critical to the decision-making process at a given point in time.
Super computers, known in the art, can potentially analyze millions of variables under alternative assumptions. As decisions are made during a disaster, each decision alters the state variables.
Although computational systems, known in the art, can accommodate changes in input, the models produced are static data models. While static models are useful during the planning phase, a static analysis may not be relevant during an actual disaster response effort, due to the dynamic nature of the underlying data and relevance of state variables.
GIS systems, known in the art, do not do multi-criteria decision analysis in real time. To optimize disaster response, it is necessary to perform an integrated assessment of multiple data layers reflecting different scenarios at various points in time, under different assumptions and parameters.